Project Summary Alopecia areata (AA) is a common autoimmune form of hair loss that affects approximately 5.3 million people in the US, including males and females across all ethnic groups. Autoimmune diseases collectively carry a tremendous public health burden, imparting a severe economic and social impact globally. The prevalence of this class of disorders is currently estimated to be 7.6-9.8%, and results in annual direct health care costs of at least $100 billion in the U.S, as estimated by the NIH. AA typically presents as loss of distinct patches of hair that can hair loss can spread to the entire scalp or the entire body. We recently conducted regulatory modeling of alopecia areata from gene expression analyses. Reverse-engineered regulatory networks have recently demonstrated great promise in the analysis of other complex diseases, such as cancer and Alzheimer?s disease, and have been used for drug prediction. The DeMAND algorithm was designed to leverage high-throughput drug screens conducted at Columbia University, where panels of FDA-approved drugs were used to treat several cancer cell lines. We utilized the DeMAND algorithm interrogated with our regulatory networks from alopecia areata patient samples, and identified a repurposable drug candidate, Vorinostat, an HDAC inhibitor, which we will evaluate for pre-clinical efficacy in alopecia areata in this proposal. There is a strong use-case for Vorinostat in alopecia areata, particularly because this predicted repurposing crosses indications. This therapeutic/indication pair represents an example of a drug developed for cancer that is being proposed for the treatment of autoimmunity. The goal of this proposal is the characterization and validation of the potential of Vorinostat to treat alopecia areata, as a therapeutic/indication pair as well as to design a clinical study to evaluate the efficacy of Vorinostat in human patients.